Pasture Soil Fertility Research Articles

Comparative Analysis of Pasture Soil Fertility in Semiarid Agro-Silvo-Pastoral Systems

Dryland pastures are usually on soils with a low fertility. However, a high spatial variability is apparent in pasture soils. In consequence, the application of inputs should be based on the particular characteristics of each field and their within-field variability. In this study, a comparative analysis of seven experimental fields (agro–silvo–pastoral ecosystems) in the southwest of the Iberian Peninsula was performed using a probabilistic and objective model. Some soil properties (the texture, cation exchange capacity, soil apparent electrical conductivity, organic matter, nitrogen, potassium and slope) measured at six sampling locations in each field were consolidated in the model. It was verified that the data fit the model and, later, a ranking of all the sampling locations and, consequently, a ranking of all the experimental fields according to the pasture soil fertility was established. Moreover, another output of the model was a ranking of the soil properties according to their influence on the soil fertility: the topographical property, the slope, was the most influential as it is related to the level of other important soil properties, as the textural components. Two fields, Cubillos and Grous, had the highest overall soil fertility levels because the soil samples in these fields were very fertile. However, some samples in the other five fields were also in the group of the most fertile, denoting the existence of an important spatial variability within each field. The proposed method is a rational way to improve the efficiency of the use of fertilisers by adjusting them to the soil pasture fertility.

Comparative Analysis of Pasture Soil Fertility in Semiarid Agro-Silvo-Pastoral Systems

Comparative Analysis of Pasture Soil Fertility in Semiarid Agro-Silvo-Pastoral Systems

Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.

Estimating and mapping pasture soil fertility in a portuguese montado based on a objective model and geostatistical techniques

Pasture soils can exhibit a high spatial variability which should be characterised to properly manage the yield potential of different within-field areas. Thus, with the aim of proposing an objective methodology to estimate the pasture soil fertility and, later, analyse its spatial pattern, the formulation of the probabilistic Rasch model constitutes a new approach in pasture fields.In this research, a case study was performed to illustrate the proposed method. Consequently, after taking some soil samples (34) and measuring different soil properties (sand, silt, and clay content, organic matter, phosphorus, potassium, moisture content, soil apparent electrical conductivity, elevation, and slope), the use of the Rasch model provides a integrated measure of pasture soil fertility at each sampling location, which can be computed using geostatistical algorithms to map its spatial distribution throughout the field.After verifying that data fit the model reasonably, the main outputs of the Rasch model were a ranking of all sampling locations according to the pasture soil fertility and another ranking of the soil properties according to their influence on the soil fertility, being the topographical properties (slope and elevation) the most influential. Later, the ordinary kriging algorithm was utilised to estimate soil fertility throughout the pasture field and the probability kriging algorithm was used to provide information for hazard assessment of pasture soil fertility, being both kriged maps the basis to delineate homogeneous zones. Finally, vegetation indices and pasture yield data at sampling points were employed to check that two zones previously determined were different.The analysis of zonal differences in pasture systems can lead to an optimal application of inputs and a more cost-effective management, with the associated environmental, economic, and energetic benefits.